Remote Sensing Estimation of European NEP Based on Improved CASA Model and Soil Respiration Model

Abstract

Net ecosystem productivity (NEP) plays an important role in understanding ecosystem functions and the global carbon cycle. In this paper, Carnegie Ames Stanford approach (CASA) model is used to estimate net primary productivity (NPP), and then coupled with soil respiration model and soil respiration heterotrophic respiration relationship model to estimate net ecosystem productivity (NEP). On this basis, this paper further improves the maximum light energy conversion rate and the optimal temperature in CASA model, so as to estimate the monthly NEP in Europe in 2014 and analyze its spatial-temporal pattern. The results show that the R2 of the optimized CASA model increases from 0.2493 to 0.4256, indicating that improving the maximum light energy conversion and the optimal temperature to optimize the CASA model is an effective method to improve the accuracy of NEP estimation. In addition, this paper analyzes the temporal and spatial distribution of European NEP, and finds that the monthly change of European NEP shows a unimodal curve with summer as the peak, which generally shows the pattern of Western Europe > Southern Europe > Central Europe > Eastern Europe> Northern Europe. This study can deepen the understanding of the distribution of carbon sources / sinks in Europe and provide a reference for the study of regional carbon cycle.